Utilization of the Veterans Affairs’ Transgender E-consultation Program by Health Care Providers: Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: In 2015, the Department of Veterans Affairs (VA) nationally implemented a transgender e-consultation (e-consult) program with expert clinical guidance for providers. OBJECTIVE: This mixed-methods project aimed to describe providers' program experiences, reasons for nonuse of the program, and ways to improve the program use. METHODS: From January to May 2017, 15 urban and rural VA providers who submitted at least one e-consult in the last year participated in semistructured interviews about their program experiences, which were analyzed using content analysis. From November to December 2017, 53 providers who encountered transgender patients but did not utilize the program participated in a brief online survey on the reasons for nonuse of the program and the facilitators encouraging use. RESULTS: Qualitative analysis showed that providers learned of the program through email; colleagues; the electronic health record (EHR) system; and participation in the VA Lesbian, Gay, Bisexual, and Transgender committees or educational trainings. Providers used the program to establish care plans, hormone therapy recommendations, sexual and reproductive health education, surgical treatment education, patient-provider communication guidance, and second opinions. The facilitators of program use included understandable recommendations, ease of use through the EHR system, and status as the only transgender resource for rural providers. Barriers to use included time constraints, communication-related problems with the e-consult, impractical recommendations for underresourced sites, and misunderstanding of the e-consult purpose. Suggestions for improvement included addition of concise or sectioned responses, expansion of program awareness among providers or patients, designation of a follow-up contact person, and increase in provider education about transgender veterans and related care. Quantitative analysis showed that the common reasons for nonuse of the program were no knowledge of the program (54%), no need of the program (32%), and receipt of help from a colleague outside of e-consult (24%). Common suggestions to improve the program use in quantitative analyses included provision of more information about where to find e-consult in the chart, guidance on talking with patients about the program, and e-mail announcements to improve provider awareness of the program. Post hoc exploratory analyses showed no differences between urban and rural providers. CONCLUSIONS: The VA transgender e-consult program is useful for providers, but there are several barriers to implementing recommendations, some of which are especially challenging for rural providers. Addressing the identified barriers and enhancing the facilitators may improve program use and quality care for transgender veterans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".